Architecture
Knowledge Graph Intelligence
AI that reasons across complex relationships -- who owns what, who reports to whom, what connects to what.
The Business Problem
Your organization's most valuable knowledge isn't in any single document -- it's in the connections between documents. Who acquired which company? Which products compete with which? Who manages the account that's connected to the flagged transaction?
These questions require traversing relationships: company to acquisition to subsidiary to product to competitor. Standard search returns documents that mention these entities. Vector databases find semantically similar content. But neither can follow a chain of connections.
Your analysts answer these questions by manually connecting dots across spreadsheets, databases, and document repositories. It takes hours, and they miss connections that span departments or data sources.
How It Solves It
Knowledge Graph Intelligence extracts entities and relationships from your documents and stores them in a graph database that the AI can query.
Simplified Flow
Ingest Documents
Extract Entities
Build Graph
Receive Question
Traverse & Answer
During ingestion, the AI reads unstructured documents and extracts structured knowledge: entities (people, companies, products, locations) and relationships (acquired, manages, competes with, supplies to). These are stored as nodes and edges in a graph database.
During querying, the user asks a natural language question. The AI translates it into a graph query, executes it against the knowledge graph, traverses the relevant relationships, and synthesizes a natural language answer.
Key Capabilities
Automatic knowledge extraction
Entities and relationships are extracted from unstructured documents automatically
Multi-hop traversal
Answers questions that require following chains of relationships across 2, 3, 4, or more hops
Natural language interface
Users ask questions in plain English; the AI translates to graph queries transparently
Incremental updates
New documents add to the graph without rebuilding from scratch
Relationship visualization
Graph structure can be visualized for exploration and validation
Audit trail
Every answer shows the exact traversal path through the graph, enabling verification
Industry Applications
Financial Services — Fraud Detection Networks
Map relationships between accounts, transactions, and entities. Detect suspicious patterns through graph traversal: "Show me all accounts within 3 hops of this flagged transaction."
Legal — Discovery & Research
Map relationships between parties, contracts, and events across large document collections. Answer questions like "What other contracts involve parties connected to the defendant?"
Healthcare — Drug Discovery
Map gene-protein-disease-drug relationships from research literature. Find novel therapeutic targets by traversing multi-hop biological pathways.
Technology & SaaS — Enterprise Knowledge Management
Build organizational knowledge graphs from emails, reports, wikis, and meeting notes. Answer questions like "Who has worked on projects related to cloud migration for financial services clients?"
Ideal For
- • Multi-hop relational queries that require traversing connections between entities
- • Complex investigations that span multiple data sources and document collections
- • Knowledge domains with rich, structured relationships (corporate structures, supply chains, regulatory networks)
- • Situations where understanding connections is more important than finding individual documents
Consider Alternatives When
- • Queries are simple keyword lookups (vector search is cheaper and faster)
- • Relationships are fluid and constantly changing (graph maintenance overhead may not be worthwhile)
- • The knowledge is personal/conversational rather than institutional (use Persistent Memory AI)
- • You need real-time external data, not internal knowledge (use Real-Time Data Access)
Knowledge Graph Intelligence vs. Persistent Memory AI
Knowledge Graph stores organizational knowledge as entity-relationship structures. Persistent Memory stores individual user interaction history. Think of Knowledge Graph as an enterprise database and Persistent Memory as a personal CRM record.
| Knowledge Graph Intelligence | Persistent Memory AI | |
|---|---|---|
| Scope | Organization-wide | Per-user |
| Structure | Entities + relationships (graph) | Conversation summaries + facts (vector) |
| Query type | "How are X and Y connected?" | "What do we know about this user?" |
| Data source | Documents, databases | User conversations |
| Infrastructure | Graph database (Neo4j) | Vector store (FAISS) |
Implementation Overview
Typical Deployment
6-10 weeks
Integration Points
Document ingestion pipelines, graph database, natural language query interface
Data Requirements
Document corpus for initial knowledge extraction; entity and relationship type definitions for your domain
Configuration
Entity types, relationship types, extraction prompts, query translation rules
Infrastructure
Graph database (Neo4j or compatible), document processing pipeline, query interface
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